
We propose cycle loss, a novel training scheme and consistency loss for motion forecasting. This loss explicitly ensures that the future trajectories predicted by the model are coherent with the history observations.
We also introduce the concept of ground truth trajectory mixing, which is necessary for cycle loss to work.

We conduct extensive experimentation on the Argoverse dataset [1]}. We justify the design choices needed for cycle loss to work through ablations and demonstrate that cycle loss can improve the performance of competitive motion forecasting models.

